{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cartpole.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "VYNA79KmgvbY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Copyright 2019 The Dopamine Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
      ]
    },
    {
      "metadata": {
        "id": "emUEZEvldNyX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Dopamine: How to train an agent on Cartpole\n",
        "\n",
        "This colab demonstrates how to train the DQN and C51 on Cartpole, based on the default configurations provided.\n",
        "\n",
        "The hyperparameters chosen are by no mean optimal. The purpose of this colab is to illustrate how to train two\n",
        "agents on a non-Atari gym environment: cartpole.\n",
        "\n",
        "We also include default configurations for Acrobot in our repository: https://github.com/google/dopamine\n",
        "\n",
        "Run all the cells below in order."
      ]
    },
    {
      "metadata": {
        "id": "Ckq6WG-seC7F",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# @title Install necessary packages.\n",
        "!pip install --upgrade --no-cache-dir dopamine-rl\n",
        "!pip install gin-config"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WzwZoRKxdFov",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# @title Necessary imports and globals.\n",
        "\n",
        "import numpy as np\n",
        "import os\n",
        "from dopamine.discrete_domains import run_experiment\n",
        "from dopamine.colab import utils as colab_utils\n",
        "from absl import flags\n",
        "import gin.tf\n",
        "\n",
        "BASE_PATH = '/tmp/colab_dopamine_run'  # @param"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bidurBV0djGi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Train DQN"
      ]
    },
    {
      "metadata": {
        "id": "PUBRSmX6dfa3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# @title Load the configuration for DQN.\n",
        "\n",
        "DQN_PATH = os.path.join(BASE_PATH, 'dqn')\n",
        "# Modified from dopamine/agents/dqn/config/dqn_cartpole.gin\n",
        "dqn_config = \"\"\"\n",
        "# Hyperparameters for a simple DQN-style Cartpole agent. The hyperparameters\n",
        "# chosen achieve reasonable performance.\n",
        "import dopamine.discrete_domains.gym_lib\n",
        "import dopamine.discrete_domains.run_experiment\n",
        "import dopamine.agents.dqn.dqn_agent\n",
        "import dopamine.replay_memory.circular_replay_buffer\n",
        "import gin.tf.external_configurables\n",
        "\n",
        "DQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n",
        "DQNAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n",
        "DQNAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n",
        "DQNAgent.network = @gym_lib.CartpoleDQNNetwork\n",
        "DQNAgent.gamma = 0.99\n",
        "DQNAgent.update_horizon = 1\n",
        "DQNAgent.min_replay_history = 500\n",
        "DQNAgent.update_period = 4\n",
        "DQNAgent.target_update_period = 100\n",
        "DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon\n",
        "DQNAgent.tf_device = '/gpu:0'  # use '/cpu:*' for non-GPU version\n",
        "DQNAgent.optimizer = @tf.train.AdamOptimizer()\n",
        "\n",
        "tf.train.AdamOptimizer.learning_rate = 0.001\n",
        "tf.train.AdamOptimizer.epsilon = 0.0003125\n",
        "\n",
        "create_gym_environment.environment_name = 'CartPole'\n",
        "create_gym_environment.version = 'v0'\n",
        "create_agent.agent_name = 'dqn'\n",
        "TrainRunner.create_environment_fn = @gym_lib.create_gym_environment\n",
        "Runner.num_iterations = 50\n",
        "Runner.training_steps = 1000\n",
        "Runner.evaluation_steps = 1000\n",
        "Runner.max_steps_per_episode = 200  # Default max episode length.\n",
        "\n",
        "WrappedReplayBuffer.replay_capacity = 50000\n",
        "WrappedReplayBuffer.batch_size = 128\n",
        "\"\"\"\n",
        "gin.parse_config(dqn_config, skip_unknown=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WuWFGwGHfkFp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# @title Train DQN on Cartpole\n",
        "dqn_runner = run_experiment.create_runner(DQN_PATH, schedule='continuous_train')\n",
        "print('Will train DQN agent, please be patient, may be a while...')\n",
        "dqn_runner.run_experiment()\n",
        "print('Done training!')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aRkvG1Nr6Etc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Train C51"
      ]
    },
    {
      "metadata": {
        "id": "s5o3a8HX6G2A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# @title Load the configuration for C51.\n",
        "\n",
        "C51_PATH = os.path.join(BASE_PATH, 'c51')\n",
        "# Modified from dopamine/agents/rainbow/config/c51_cartpole.gin\n",
        "c51_config = \"\"\"\n",
        "# Hyperparameters for a simple C51-style Cartpole agent. The hyperparameters\n",
        "# chosen achieve reasonable performance.\n",
        "import dopamine.agents.dqn.dqn_agent\n",
        "import dopamine.agents.rainbow.rainbow_agent\n",
        "import dopamine.discrete_domains.gym_lib\n",
        "import dopamine.discrete_domains.run_experiment\n",
        "import dopamine.replay_memory.prioritized_replay_buffer\n",
        "import gin.tf.external_configurables\n",
        "\n",
        "RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n",
        "RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n",
        "RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n",
        "RainbowAgent.network = @gym_lib.CartpoleRainbowNetwork\n",
        "RainbowAgent.num_atoms = 51\n",
        "RainbowAgent.vmax = 10.\n",
        "RainbowAgent.gamma = 0.99\n",
        "RainbowAgent.update_horizon = 1\n",
        "RainbowAgent.min_replay_history = 500\n",
        "RainbowAgent.update_period = 4\n",
        "RainbowAgent.target_update_period = 100\n",
        "RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon\n",
        "RainbowAgent.replay_scheme = 'uniform'\n",
        "RainbowAgent.tf_device = '/gpu:0'  # use '/cpu:*' for non-GPU version\n",
        "RainbowAgent.optimizer = @tf.train.AdamOptimizer()\n",
        "\n",
        "tf.train.AdamOptimizer.learning_rate = 0.001\n",
        "tf.train.AdamOptimizer.epsilon = 0.0003125\n",
        "\n",
        "create_gym_environment.environment_name = 'CartPole'\n",
        "create_gym_environment.version = 'v0'\n",
        "create_agent.agent_name = 'rainbow'\n",
        "Runner.create_environment_fn = @gym_lib.create_gym_environment\n",
        "Runner.num_iterations = 50\n",
        "Runner.training_steps = 1000\n",
        "Runner.evaluation_steps = 1000\n",
        "Runner.max_steps_per_episode = 200  # Default max episode length.\n",
        "\n",
        "WrappedPrioritizedReplayBuffer.replay_capacity = 50000\n",
        "WrappedPrioritizedReplayBuffer.batch_size = 128\n",
        "\"\"\"\n",
        "gin.parse_config(c51_config, skip_unknown=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VI_v9lm66jzq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# @title Train C51 on Cartpole\n",
        "c51_runner = run_experiment.create_runner(C51_PATH, schedule='continuous_train')\n",
        "print('Will train agent, please be patient, may be a while...')\n",
        "c51_runner.run_experiment()\n",
        "print('Done training!')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hqBe5Yad63FT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Plot the results"
      ]
    },
    {
      "metadata": {
        "id": "IknanILXX4Zz",
        "colab_type": "code",
        "outputId": "e7e5b94c-2872-426b-fb69-a51365eb5fe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "# @title Load the training logs.\n",
        "data = colab_utils.read_experiment(DQN_PATH, verbose=True,\n",
        "                                   summary_keys=['train_episode_returns'])\n",
        "data['agent'] = 'DQN'\n",
        "data['run'] = 1\n",
        "c51_data = colab_utils.read_experiment(C51_PATH, verbose=True,\n",
        "                                       summary_keys=['train_episode_returns'])\n",
        "c51_data['agent'] = 'C51'\n",
        "c51_data['run'] = 1\n",
        "data = data.merge(c51_data, how='outer')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading statistics from: /tmp/colab_dopamine_run/dqn//logs/log_49\n",
            "Reading statistics from: /tmp/colab_dopamine_run/c51//logs/log_49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mSOVFUKN-kea",
        "colab_type": "code",
        "outputId": "8ec8c20a-2409-420e-a0a0-1b14907bfbed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        }
      },
      "cell_type": "code",
      "source": [
        "# @title Plot training results.\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16,8))\n",
        "sns.tsplot(data=data, time='iteration', unit='run',\n",
        "           condition='agent', value='train_episode_returns', ax=ax)\n",
        "plt.title('Cartpole')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gldQhEZGdGFitk2bFXd0j5eupwcQIH5RVt6G+RTfkz9P3mnCwuBH+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\ng0JNipIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}
